Analysis of gene expression identifies candidate markers and pharmacological targets in prostate cancer.

By J. Welsh, L. Sapinoso, Andrew I. Su, S. Kern, J. Wang-Rodriguez, C. Moskaluk, H. Frierson, G. Hampton. Cancer Research, 2001

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Combining multiple microarray studies and modeling interstudy variation (2003, methodology): “Two datasets are from cDNA technology (Dhanasekaran et al., 2001; Luo et al., 2001), while the others are from oligo-based technology (Magee et al., 2001; Welsh et al., 2001).”
Integrative genomic and proteomic analysis of prostate cancer reveals signatures of metastatic progression. (2005, unknown): “…assembled from eight publicly available prostate cancer gene expression data sets (Dhanasekaran et al., 2001; Lapointe et al., 2004; LaTulippe et al., 2002; Luo et al., 2001, 2002b; Singh et al., 2002; Welsh et al., 2001; Yu et al., 2004), and each probe was mapped to Unigene Build #173 (Table S3).”
Meta-analysis of microarrays: interstudy validation of gene expression profiles reveals pathway dysregulation in prostate cancer. (2002, methodology): “Two of the groups used spotted cDNA technology (3, 8), whereas two other groups used commercial oligonucleotide-based technology (9, 10).”
ONCOMINE: a cancer microarray database and integrated data-mining platform. (2004, unknown): “potential, and patient survival [1–24].”
Integrating structured biological data by Kernel Maximum Mean Discrepancy (2006, methodology): “We examine whether MMD can distinguish between normal and tumor tissues, using the microarray data from a prostate cancer study by Welsh et al. (2001). Again, α was set to 0.”